week11-machine-learning/random-forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a1e29f1d",
   "metadata": {},
   "source": [
    "# CICF Week 11\n",
    "\n",
    "This notebook covers decision trees and random forests, a classical machine learning technique.\n",
    "Since the CICF course is not about data science per se, our focus is not on the math behind the method, but on the packages used to do it.\n",
    "However it is uninformative to just show the steps without giving an idea of what is going on in the background, so we will cover the idea behind decision trees and in the process show the computational tools used.\n",
    "\n",
    "We will use the Titanic dataset, which is the beginner dataset on Kaggle, a machine learning learning site.\n",
    "It is a mix of categorical and numeric data.\n",
    "\n",
    "This notebook is based on [Jeremy Howard's notebook](https://www.kaggle.com/code/jhoward/how-random-forests-really-work) of the same, which I enourage everyone who is insterested in understanding the technique to look at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe9e3006",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'fastai'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mfastai\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimports\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m      2\u001b[0m np\u001b[38;5;241m.\u001b[39mset_printoptions(linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m130\u001b[39m)\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'fastai'"
     ]
    }
   ],
   "source": [
    "from fastai.imports import *\n",
    "np.set_printoptions(linewidth=130)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb7d9db2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-04-01 14:06:57--  https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 60302 (59K) [text/plain]\n",
      "Saving to: ‘titanic.csv’\n",
      "\n",
      "titanic.csv         100%[===================>]  58.89K  --.-KB/s    in 0.01s   \n",
      "\n",
      "2024-04-01 14:06:58 (5.07 MB/s) - ‘titanic.csv’ saved [60302/60302]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b0849451",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c6b63993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId                      1\n",
       "Survived                       0.0\n",
       "Pclass                         3.0\n",
       "Name           Abbing, Mr. Anthony\n",
       "Sex                           male\n",
       "Age                           24.0\n",
       "SibSp                          0.0\n",
       "Parch                          0.0\n",
       "Ticket                        1601\n",
       "Fare                          8.05\n",
       "Cabin                      B96 B98\n",
       "Embarked                         S\n",
       "LogFare                   2.202765\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modes=df.mode().iloc[0]\n",
    "modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c550213",
   "metadata": {},
   "outputs": [],
   "source": [
    "def proc_data(df):\n",
    "    df['Fare'] = df.Fare.fillna(0)\n",
    "    df.fillna(modes, inplace=True)\n",
    "    df['LogFare'] = np.log1p(df['Fare'])\n",
    "    df['Embarked'] = pd.Categorical(df.Embarked)\n",
    "    df['Sex'] = pd.Categorical(df.Sex)\n",
    "proc_data(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "519515ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "cats=['Sex','Embarked']\n",
    "conts=['Age', 'SibSp', 'Parch', 'LogFare', 'Pclass']\n",
    "dep='Survived'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "253d4def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      male\n",
       "1    female\n",
       "2    female\n",
       "3    female\n",
       "4      male\n",
       "Name: Sex, dtype: category\n",
       "Categories (2, object): ['female', 'male']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bf5d7301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fig,axs = plt.subplots(1,2,figsize=(11,5))\n",
    "sns.barplot(data=df, y=dep, x=\"Sex\", ax=axs[0]).set(title=\"Survival rate\")\n",
    "sns.countplot(data=df, x=\"Sex\", ax=axs[1]).set(title=\"Histogram\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b3087a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import random\n",
    "from sklearn.model_selection import train_test_split\n",
    "random.seed(42)\n",
    "trn_df,val_df = train_test_split(df, test_size=0.25)\n",
    "trn_df[cats] = trn_df[cats].apply(lambda x: x.cat.codes)\n",
    "val_df[cats] = val_df[cats].apply(lambda x: x.cat.codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2e08615e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def xs_y(df):\n",
    "    xs = df[cats+conts].copy()\n",
    "    return xs, df[dep] if dep in df else None\n",
    "\n",
    "trn_xs,trn_y = xs_y(trn_df)\n",
    "val_xs,val_y = xs_y(val_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e2b4141e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_xs.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ac15ef4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = val_xs.Sex==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3f01ae26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21524663677130046"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "mean_absolute_error(val_y, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6caf2978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_fare = trn_df[trn_df.LogFare>0]\n",
    "fig,axs = plt.subplots(1,2, figsize=(11,5))\n",
    "sns.boxenplot(data=df_fare, x=dep, y=\"LogFare\", ax=axs[0])\n",
    "sns.kdeplot(data=df_fare, x=\"LogFare\", ax=axs[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "44b07d66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.336322869955157"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = val_xs.LogFare>2.7\n",
    "mean_absolute_error(val_y, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ecbe1efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _side_score(side, y):\n",
    "    tot = side.sum()\n",
    "    if tot<=1: return 0\n",
    "    return y[side].std()*tot\n",
    "\n",
    "def score(col, y, split):\n",
    "    lhs = col<=split\n",
    "    return (_side_score(lhs,y) + _side_score(~lhs,y))/len(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6ba14ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40787530982063946"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score(trn_xs[\"Sex\"], trn_y, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f3f25c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47180873952099694"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score(trn_xs[\"LogFare\"], trn_y, 2.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "33e90c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Sex': (0, 0.40787530982063946),\n",
       " 'Embarked': (0, 0.47883342573147836),\n",
       " 'Age': (6.0, 0.478316717508991),\n",
       " 'SibSp': (4, 0.4783740258817434),\n",
       " 'Parch': (0, 0.4805296527841601),\n",
       " 'LogFare': (2.4390808375825834, 0.4620823937736597),\n",
       " 'Pclass': (2, 0.46048261885806596)}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def min_col(df, nm):\n",
    "    col,y = df[nm],df[dep]\n",
    "    unq = col.dropna().unique()\n",
    "    scores = np.array([score(col,y,o) for o in unq if not np.isnan(o)])\n",
    "    idx = scores.argmin()\n",
    "    return unq[idx],scores[idx]\n",
    "\n",
    "cols=cats+conts\n",
    "{o:min_col(trn_df, o) for o in cols}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "280fcf45",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "m = DecisionTreeClassifier(max_leaf_nodes=4).fit(trn_xs, trn_y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d73e3424",
   "metadata": {},
   "outputs": [],
   "source": [
    "import graphviz\n",
    "def draw_tree(t, df, size=10, ratio=0.6, precision=2, **kwargs):\n",
    "    s=export_graphviz(t, out_file=None, feature_names=df.columns, filled=True, rounded=True, special_characters=True, rotate=False, precision=precision, **kwargs)\n",
    "    return graphviz.Source(re.sub('Tree }', f'Tree {{ size={size}; ratio={ratio}', s))                     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b535e488",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_tree(m, trn_xs, size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "06047504",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tree(prop=0.75):\n",
    "    n = len(trn_y)\n",
    "    idxs =random.choice(n, int(n*prop))\n",
    "    return DecisionTreeClassifier(min_samples_leaf=5).fit(trn_xs.iloc[idxs], trn_y.iloc[idxs])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bb49831d",
   "metadata": {},
   "outputs": [],
   "source": [
    "trees = [get_tree() for t in range(100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "03a0d346",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22748878923766816"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_probs = [t.predict(val_xs) for t in trees]\n",
    "avg_probs = np.stack(all_probs).mean(0)\n",
    "mean_absolute_error(val_y, avg_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "07754a9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18834080717488788"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(100, min_samples_leaf=5)\n",
    "rf.fit(trn_xs, trn_y)\n",
    "mean_absolute_error(val_y, rf.predict(val_xs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7c0913c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(dict(cols=trn_xs.columns, imp=m.feature_importances_)).plot('cols', 'imp', 'barh');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c8c530ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>LogFare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.787834</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.442347</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.188856</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.526361</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.504848</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.380150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.169054</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.944439</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.169054</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.306275</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.990834</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.202765</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.275887</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.107178</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.639057</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.698830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.980694</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.178064</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.639057</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.970292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.185579</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.316003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.180892</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.324836</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.740840</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sex  Embarked   Age  SibSp  Parch   LogFare  Pclass  survived  prediction\n",
       "709    1         0  24.0      1      1  2.787834       3         1           0\n",
       "439    1         2  31.0      0      0  2.442347       2         0           0\n",
       "840    1         2  20.0      0      0  2.188856       3         0           0\n",
       "720    0         2   6.0      0      1  3.526361       2         1           1\n",
       "39     0         0  14.0      1      0  2.504848       3         1           1\n",
       "290    0         2  26.0      0      0  4.380150       1         1           1\n",
       "300    0         1  24.0      0      0  2.169054       3         1           1\n",
       "333    1         2  16.0      2      0  2.944439       3         0           0\n",
       "208    0         1  16.0      0      0  2.169054       3         1           1\n",
       "136    0         2  19.0      0      2  3.306275       1         1           1\n",
       "137    1         2  37.0      1      0  3.990834       1         0           0\n",
       "696    1         2  44.0      0      0  2.202765       3         0           0\n",
       "485    0         2  24.0      3      1  3.275887       3         0           0\n",
       "244    1         0  30.0      0      0  2.107178       3         0           0\n",
       "344    1         2  36.0      0      0  2.639057       2         0           0\n",
       "853    0         2  16.0      0      1  3.698830       1         1           1\n",
       "621    1         2  42.0      1      0  3.980694       1         1           0\n",
       "653    0         1  24.0      0      0  2.178064       3         1           1\n",
       "886    1         2  27.0      0      0  2.639057       2         0           0\n",
       "110    1         2  47.0      0      0  3.970292       1         0           0\n",
       "294    1         2  24.0      0      0  2.185579       3         0           0\n",
       "447    1         2  34.0      0      0  3.316003       1         1           0\n",
       "192    0         2  19.0      1      0  2.180892       3         1           0\n",
       "682    1         2  20.0      0      0  2.324836       3         0           0\n",
       "538    1         2  24.0      0      0  2.740840       3         0           0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_xs.assign(survived=val_y, prediction=rf.predict(val_xs))[:25]"
   ]
  }
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